Neutrino flavor instabilities in a time-dependent supernova model

A dense neutrino medium such as that inside a core-collapse supernova can experience collective flavor conversion or oscillations because of the neutral-current weak interaction among the neutrinos. This phenomenon has been studied in a restricted, stationary supernova model which possesses the (spatial) spherical symmetry about the center of the supernova and the (directional) axial symmetry around the radial direction. Recently it has been shown that these spatial and directional symmetries can be broken spontaneously by collective neutrino oscillations. In this paper we analyze the neutrino flavor instabilities in a time-dependent supernova model. Our results show that collective neutrino oscillations start at approximately the same radius in both the stationary and time-dependent supernova models unless there exist very rapid variations in local physical conditions on timescales of a few microseconds or shorter. Our results also suggest that collective neutrino oscillations can vary rapidly with time in the regimes where they do occur which need to be studied in time-dependent supernova models.Comment: 5 pages, 2 figures, version to appear in PL

Applications of Machine Learning to Detecting Fast Neutrino Flavor Instabilities in Core-Collapse Supernova and Neutron Star Merger Models

Neutrinos propagating in a dense neutrino gas, such as those expected in core-collapse supernovae (CCSNe) and neutron star mergers (NSMs), can experience fast flavor conversions on relatively short scales. This can happen if the neutrino electron lepton number ($\nu$ELN) angular distribution crosses zero in a certain direction. Despite this, most of the state-of-the-art CCSN and NSM simulations do not provide such detailed angular information and instead, supply only a few moments of the neutrino angular distributions. In this study we employ, for the \emph{first} time, a machine learning (ML) approach to this problem and show that it can be extremely successful in detecting $\nu$ELN crossings on the basis of its zeroth and first moments. We observe that an accuracy of $\sim95\%$ can be achieved by the ML algorithms, which almost corresponds to the Bayes error rate of our problem. Considering its remarkable efficiency and agility, the ML approach provides one with an unprecedented opportunity to evaluate the occurrence of FFCs in CCSN and NSM simulations \emph{on the fly}. We also provide our ML methodologies on \href{https://github.com/sajadabbar/ML-nu_FFI/tree/main}{GitHub}.Comment: 7 pages, 5 figures, submitted to PRD. Methodologies are available on GitHub: https://github.com/sajadabbar/ML-nu_FFI/tree/mai

Topics in the Physics of Supernovae and Neutron Stars

A star with a mass larger than 8−10 solar masses can end its life in a supernova ex- plosion and possibly form a neutron star. In this dissertation, I study two important aspects of the physics of supernovae and neutron stars. In the first part, I consider neutrino flavor oscillations in supernovae. Neutrino flavor oscillations in the presence of ambient neutrinos is nonlinear in nature which leads to interesting phenomenology that has not been well understood. This phenomenon in the supernova context has been studied in the so-called neutrino Bulb model which is a restricted, stationary supernova model and which possesses the (spatial) spherical symmetry about the center of the supernova and the (directional) axial symmetry around the radial direction. By studying the problem of the neutrino oscillations in a two dimensional toy model, the so-called neutrino Line model, I show that the spatial symmetries can be broken spontaneously in a dense neutrino gas. Using a time-dependent version of the neutrino Bulb model, I also show that the stationarity of a neutrino gas can be broken spontaneously as well. In the second part, I compute the thermal conductivity of the neutron star crust. I use the quantum Monte Carlo (QMC) technique to calculate the static structure function S(q) of a one-component ion lattice and use it to compute the thermal conductivity κ of high-density solid matter expected in the neutron star crust. By making detailed comparisons with the results obtained using one-phonon approximation (OPA), and the multi-phonon harmonic approximation, we assess the temperature regime where S(q) from QMC can be used directly to calculate κ. We also com- pare the QMC results to those obtained using the classical Monte Carlo technique to quantitatively assess the magnitude of the quantum corrections. We show that the quantum effects become relevant at temperature T \u3c 0.3 ΩP, where ΩP is the ion plasma frequency. At T ≃ 0.1 ΩP the quantum effects suppress κ by about 30%. The comparison with the results of the OPA indicates that dynamical information beyond the static structure is needed when T \u3c 0.1 ΩP

Using Twitter to Understand Public Interest in Climate Change: The case of Qatar

Climate change has received an extensive attention from public opinion in the last couple of years, after being considered for decades as an exclusive scientific debate. Governments and world-wide organizations such as the United Nations are working more than ever on raising and maintaining public awareness toward this global issue. In the present study, we examine and analyze Climate Change conversations in Qatar's Twittersphere, and sense public awareness towards this global and shared problem in general, and its various related topics in particular. Such topics include but are not limited to politics, economy, disasters, energy and sandstorms. To address this concern, we collect and analyze a large dataset of 109 million tweets posted by 98K distinct users living in Qatar -- one of the largest emitters of CO2 worldwide. We use a taxonomy of climate change topics created as part of the United Nations Pulse project to capture the climate change discourse in more than 36K tweets. We also examine which topics people refer to when they discuss climate change, and perform different analysis to understand the temporal dynamics of public interest toward these topics.Comment: Will appear in the proceedings of the International Workshop on Social Media for Environment and Ecological Monitoring (SWEEM'16

A Lesson Study of Internet Usage to Enhance the Development of English Language Teaching in a Libyan University

The research discussed in this thesis is based upon a programme of study in a Libyan university, which focused on the use of the Internet in the classroom in order to enhance English language teaching and learning. In the last few decades, information and communication technology (ICT) has strongly influenced society as well as education as it has become a part of daily life, offering access to a world of knowledge. This thesis describes, through a single case study, how three teachers at the University of Benghazi collaborated in the use of a ‘Lesson Study’ approach, in order to engage a group of English as Foreign Language (EFL) students in an e-learning teaching programme. The Lesson Study approach is a technique in Action Research whereby teachers work collaboratively to improve their pedagogy by observing the teaching as well as the learning as they teach students. This study explores university rationales for using ICT, by means of a case study in which myself, teachers and school managers were engaged in a pilot project which implemented ICT in teaching. My interest in researching this topic started while working at the University of Benghazi as an EFL teacher, as described above. In this role, I had an interest in contributing to the improvement of teaching practices in Libya in general and the University of Benghazi in particular. I began to do this by improving my own pedagogical practices and by creating opportunities for developing practice within the department. The study was prompted by a set of concerns that emerged as a result of my own English language teaching practice at my university. I found that even though students were happy to learn English, they could not practise the language in authentic situations, because in Libya English is not generally spoken outside the classroom. I discuss a curriculum initiative devised to tackle these concerns, directed towards engaging students to learn in a technology-based, collaborative, cognitively demanding and intercultural way. The broad aim of the study was to investigate how Libyan students’ current English literacy practices might be enhanced by using the Internet and E-learning strategies and how the Internet may be used as a medium to further assist the students’ English learning development. Through an extensive and in- depth use of literature, drawing on journals, articles, books and previous research studies, this thesis also explores some of the possibilities of the Internet in developing differing styles of classroom pedagogy and the 7 implications of incorporating the Internet into existing programme design and curriculum. It also reports on the way in which Lesson Study was used as a professional development strategy in a new setting and to discuss its effectiveness in research. In terms of the methodology, interview data was combined with questionnaire data and analysed. Support was found for a preparative rationale, a pedagogical rationale and a motivational rationale among teachers and students. Some limitations in this study needed to be bridged in order to build a widely supported vision and policy plan on conducting this thesis. The significant findings from this study include the observation that the lesson studies process contributed to bringing about change in teacher pedagogy. The major issues highlighted by these findings include the need to make changes to teacher practice and the way that the Lesson Study programme, as an Action Research model, impacted directly and positively upon teacher pedagogy, with an observed increase in student motivation for learning. These findings therefore have implications for the teachers of Higher Education in Libya if they are going to make sustainable pedagogical changes that will positively impact on student learning and outcomes

Detecting Fast Neutrino Flavor Conversions with Machine Learning

Neutrinos in dense environments like core-collapse supernovae (CCSNe) and neutron star mergers (NSMs) can undergo fast flavor conversions (FFCs) once the angular distribution of neutrino lepton number crosses zero along a certain direction. Recent advancements have demonstrated the effectiveness of machine learning (ML) in detecting these crossings. In this study, we enhance prior research in two significant ways. Firstly, we utilize realistic data from CCSN simulations, where neutrino transport is solved using the full Boltzmann equation. We evaluate the ML methods' adaptability in a real-world context, enhancing their robustness. In particular, we demonstrate that when working with artificial data, simpler models outperform their more complex counterparts, a noteworthy illustration of the bias-variance tradeoff in the context of ML. We also explore methods to improve artificial datasets for ML training. In addition, we extend our ML techniques to detect the crossings in the heavy-leptonic channels, accommodating scenarios where $\nu_x$ and $\bar\nu_x$ may differ. Our research highlights the extensive versatility and effectiveness of ML techniques, presenting an unparalleled opportunity to evaluate the occurrence of FFCs in CCSN and NSM simulations.Comment: Submitted to PR